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SmolLM2-135M-Instruct-bnb-4…/README.md
ModelHub XC 0c6ac09529 初始化项目,由ModelHub XC社区提供模型
Model: Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning
Source: Original Platform
2026-07-29 06:24:11 +08:00

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---
license: apache-2.0
language:
- en
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
- smollm2
- reasoning
- chain-of-thought
- instruct
- lora
- merged
datasets:
- Avtrkrb/combined-reasoning
---
# SmolLM2-135M-Instruct-Reasoning
A reasoning-focused fine-tune of SmolLM2-135M-Instruct trained using Unsloth on the combined-reasoning dataset.
## Model Overview
This model was created by fine-tuning SmolLM2-135M-Instruct on a reasoning-oriented dataset containing step-by-step solutions and structured problem-solving examples.
The model is intended to:
- Produce detailed reasoning traces
- Explain intermediate steps
- Perform multi-step logical reasoning
- Improve instruction following
- Generate transparent solutions instead of only final answers
## Base Model
HuggingFaceTB/SmolLM2-135M-Instruct
## Dataset
Avtrkrb/combined-reasoning
## Training Method
- Framework: Unsloth
- Fine-tuning type: LoRA
- Task: Supervised Fine-Tuning (SFT)
## Prompt Format
This model uses the standard SmolLM2 chat template.
<|im_start|>system
You are a helpful AI assistant named SmolLM, trained by Hugging Face
<|im_end|>
<|im_start|>user
What is 17 × 23?
<|im_end|>
<|im_start|>assistant
## Example Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
## Intended Use
- Reasoning tasks
- Educational explanations
- Problem solving
- Step-by-step analysis
## Limitations
- Small 135M parameter model
- Reasoning quality is limited compared to larger models
- May hallucinate facts
- Should not be used for high-stakes decisions
## License
Apache 2.0 (inherits base model licensing)